A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study optimal portfolio selection with Recovery Average Value at Risk, showing better control over liabilities.
New risk measure improves creditor protection in financial regulation.
New method assesses financial and cyber risks under uncertainty.
New versions of the set-valued average value at risk for multivariate risks are introduced by generalizing the well-known certainty equivalent representation to the set-valued case. The first "regulator" version is independent from any market model whereas the second version, called the market extension, takes trading …
The paper studies the convergence of SAA for systemic risk measures.
Paper approximates risk measures using SGD with Langevin dynamics.
Proposes a new method for determining LGD discount rates based on cost of capital.
Study improves accuracy of risk measures using advanced algorithms.
In structural credit risk models, default events and the ensuing losses are both derived from the asset values at maturity. Hence it is of utmost importance to choose a distribution for these asset values which is in accordance with empirical data. At the same time, it is desirable to still preserve some analytical tra…
Optimizes loan recovery timing across various portfolios.
The study uses Random Matrix Theory to identify structural changes in stock markets during shocks.
Develops a new method for robust risk measurement by averaging nearby payoffs.
Paper studies convex risk measures linked to optimization.
Improved tail risk forecasting model for assets using CAViaR with spillover effects.
SAA method solves insurance portfolio optimization with CVaR constraints.
This thesis presents the Conditional Value-at-Risk concept and combines an analysis that covers its application as a risk measure and as a vector norm. For both areas of application the theory is revised in detail and examples are given to show how to apply the concept in practice. In the first part, CVaR as a risk mea…
Unexpectedly, weighted Pareto variables are stochastically dominant.
Study shows equivalence of four risk constraints in non-concave optimization problems.
The paper studies risk-sensitive learning schemes and provides learning bounds for empirical OCE minimizers.
In this paper we study time-consistent risk measures for returns that are given by a GARCH(1,1) model. We present a construction of risk measures based on their static counterparts that overcomes the lack of time-consistency. We then study in detail our construction for the risk measures Value-at-Risk (VaR) and Average…
The paper introduces a new method for forecasting financial risk using quantile-based modeling.
In a wide variety of sequential decision making problems, it can be important to estimate the impact of rare events in order to minimize risk exposure. A popular risk measure is the conditional value-at-risk (CVaR), which is commonly estimated by averaging observations that occur beyond a quantile at a given confidence…
Diversified risk parity strategies outperform equally-weighted portfolios in various asset universes.
Accounting for model uncertainty in risk management and option pricing leads to infinite dimensional optimization problems which are both analytically and numerically intractable. In this article we study when this hurdle can be overcome for the so-called optimized certainty equivalent risk measure (OCE) -- including t…
Risk measures for multivariate financial positions are studied in a utility-based framework. Under a certain incomplete preference relation, shortfall and divergence risk measures are defined as the optimal values of specific set minimization problems. The dual relationship between these two classes of multivariate ris…
A method for calculating multi-portfolio time consistent multivariate risk measures in discrete time is presented. Market models for assets with transaction costs or illiquidity and possible trading constraints are considered on a finite probability space. The set of capital requirements at each time and state is c…
Study shows overparameterization helps shallow neural networks recover signals in high dimensions.
Study improves risk management for volatile markets using expectiles.
SVR analyzed within RQ framework for risk management.
Solves VaR-constrained portfolio optimization in markets with stochastic volatility.
We propose a fast algorithm for computing the economic capital, Value at Risk and Greeks in the Gaussian factor model. The algorithm proposed here is much faster than brute force Monte Carlo simulations or Fourier transform based methods \cite{MD}. While the algorithm of Hull-White \cite{HW} is comparably fast, it assu…
The paper explores how market trade values and volumes affect price and return statistics.
The objective in a traditional reinforcement learning (RL) problem is to find a policy that optimizes the expected value of a performance metric such as the infinite-horizon cumulative discounted or long-run average cost/reward. In practice, optimizing the expected value alone may not be satisfactory, in that it may be…
Options are generally learned by using an inaccurate environment model (or simulator), which contains uncertain model parameters. While there are several methods to learn options that are robust against the uncertainty of model parameters, these methods only consider either the worst case or the average (ordinary) case…
The paper improves Monte Carlo methods for optimization problems.
In this article, we study the problem of pricing defaultable bond with discrete default intensity and barrier under constant risk free short rate using higher order binary options and their integrals. In our credit risk model, the risk free short rate is a constant and the default event occurs in an expected manner whe…
This work tackles risk-sensitive deep RL by optimizing policies with variance constraints.
Study on risk measures using distorted Choquet integrals with random distortions.
We present a method to obtain the average and the typical value of the number of critical points of the empirical risk landscape for generalized linear estimation problems and variants. This represents a substantial extension of previous applications of the Kac-Rice method since it allows to analyze the critical points…
Develops a new risk measure for Markov chains' asymptotic behavior.
Under Solvency II the computation of capital requirements is based on value at risk (V@R). V@R is a quantile-based risk measure and neglects extreme risks in the tail. V@R belongs to the family of distortion risk measures. A serious deficiency of V@R is that firms can hide their total downside risk in corporate network…
We discuss the coherence properties of Expected Shortfall (ES) as a financial risk measure. This statistic arises in a natural way from the estimation of the "average of the 100p % worst losses" in a sample of returns to a portfolio. Here p is some fixed confidence level. We also compare several alternative representat…
Predictions are issued on the basis of certain information. If the forecasting mechanisms are correctly specified, a larger amount of available information should lead to better forecasts. For point forecasts, we show how the effect of increasing the information set can be quantified by using strictly consistent scorin…
It is well known that Expected Shortfall (also called Average Value-at-Risk) is a convex risk measure, i. e. Expected Shortfall of a convex linear combination of arbitrary risk positions is not greater than a convex linear combination with the same weights of Expected Shortfalls of the same risk positions. In this shor…
Equivalent characterizations of multiportfolio time consistency are deduced for closed convex and coherent set-valued risk measures on with image space in the power set of . In the convex case, multiportfolio time consistency is equivalent to a cocycle condition on…
The paper assesses the risk of negative treatment effects using bounds and inference.
It is a well known fact that recovery rates tend to go down when the number of defaults goes up in economic downturns. We demonstrate how the loss given default model with the default and recovery dependent via the latent systematic risk factor can be estimated using Bayesian inference methodology and Markov chain Mont…
In this work we consider optimal stopping problems with conditional convex risk measures called optimised certainty equivalents. Without assuming any kind of time-consistency for the underlying family of risk measures, we derive a novel representation for the solution of the optimal stopping problem. In particular, we …